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Online job scams seem increasingly common. I'm curious whether people would actually use a job board where every job listing is verified before being published. Would something like this make you more likely to search for jobs there?

do you keep up with writing syntax? do you do it as a hobby or try to weave it into work? do you do it from scratch or only when it's easier than prompting?

I’m ready to retire. In my younger days, I remember a few pivotal moments for me as a young nerd. Active Server Pages. COM components. VB6. I know these are laughable today but back then it was the greatest thing in the world to be able to call server-side commands. It kept me up nights trying to absorb it all. Fast forward decades and Claude Code is giving me that same energy and drive. I love it. It feels like it did back then. I’m chasing the midnight hour and not getting any sleep.

I am VE3HWO. I hold a basic with honours and advanced qualifications in Canada. Hoping to connect with other hams on HN. 73

How can I deal with this in front of employers or collaborators without giving exact details, which are private in nature? I asked an AI, and it said giving direct responses was a good idea but I think in cases such as mine people are more likely to victim blame.

Other details, I didn’t quit or take a leave so no one knows. I am bringing this question up now because a professor has literally remarked that my publication record is “thin”.

Hi everyone,

I built Whisker because setting up a simple online store shouldn't require WordPress, Composer, or $39/month SaaS fees. It's a self hosted e-commerce cart in pure PHP + MySQL with no external dependencies.

Upload to any shared hosting, run the 6-step web installer, start selling. That's it.

What it does: product catalog with variants (Size × Color with individual SKU/price/stock per combo), 4 payment gateways (Razorpay, Stripe, CCAvenue, crypto), multi-currency, admin dashboard, order management, invoices, customer accounts, SEO engine with JSON-LD product schema, CSV bulk import, support tickets, coupon system. Security: PDO prepared statements, bcrypt, CSRF, session fingerprinting, webhook signature verification, login rate limiting, upload MIME+extension validation.

~130 files, 25 tables, runs on PHP 8.0+ and MySQL 5.7+.

Demo: https://whisker.lohit.me

GitHub: https://github.com/WhiskerEnt/Whisker-Cart

I'd appreciate any feedback on the architecture or security. Happy to discuss technical decisions.

Well first, I don't mean this as a shot at the mods that do a great job.

I'm noticing a lot of posts and comments around AI seem like industry plants. Look, I recognize that people are excited about AI and want to share their perspectives, and I'm not trying to accuse people that disagree with me of being industry plants, but the recent one about "I love coding at 60 now because of Claude Code!" by a new user with "cc" at the end of their name seems a LITTLE suspicious. As is the number of upvotes -- I can understand how a new model gets a lot of excitement, but a stranger on the internet enjoying the new model doesn't seem like 800 points worthy?

I'm not trying to spark a controversy here, but I'm wondering if others feel the same or if I'm just overreacting.

I'm curious how teams running LLM-heavy applications handle duplicate or redundant API calls in production.

While experimenting with LLM APIs, I noticed that the same prompt can sometimes be sent repeatedly across different parts of an application, which leads to unnecessary token usage and higher API costs.

For teams using OpenAI, Anthropic, or similar APIs in production: How do you currently detect or prevent duplicate prompts or redundant calls? Do you rely on logging and dashboards, caching layers, internal proxy services, or something else? Or is this generally considered a minor issue that most teams just accept as part of normal usage?

Many have heard the news that Google plans to block users from installing APKs not signed by Google. But Google hasn't changed its mind yet. I'm wondering if there are people here who know much more than we do. How do you plan to handle this situation if it's not a threat, but a real plan from Google?"

I've been on HN under different aliases since 2010 and over the last couple of years I feel like the quality of HN has nosed dived and so has my enjoyment.

For the first time ever I questioned today whether I should continue to use HN anymore so I'm writing this partly to explore my own thoughts and to see if anyone else feels similarly.

1. AI, AI, AI.

I get it. AI is the big thing right now, but I find AI posts fundamentally less interesting than the traditional tech content that used to be posted here. A post containing someone's qualitative opinion on how different AI models compare when drawing pelicans simply isn't as technically interesting as something like this, https://nee.lv/2021/02/28/How-I-cut-GTA-Online-loading-times-by-70/

2. Does any build startups here anymore?

Again, I get it. I largely quit trying to bootstrap my own startup ideas in the late 2010s. The industry became too competitive for a solo founder without significant financial backing to have much of a chance of success. And today it's even harder. But I think this has changed HN from a place where you used to frequently see people launching cool new projects to a place where people just discuss the latest big tech AI model launch.

3. Politicisation and intolerance

One of the things I've always liked about HN was that it's a very open minded place. And it still is in many ways, especially when compared to other platforms like X and Reddit, but even here I've noticed comments becoming more one-sided and those with less popular opinions more frequently being flagged and downvoted.

Perhaps it's just me, but I never downvote or flag people unless I genuinely think their comment is cruel or aggressively disregarding the guidelines.

4. Is it just me?

I know I've become increasingly nostalgic to the internet I grew up with... Everything was so much more exciting back then, and yet everything felt so in reach. Sites like YouTube were revolutionary yet built by just three people. Same with sites like MySpace and Facebook which again were hacked together by a handful of people, at least in the early days.

Today things rarely feel new and everything feels so far from reach. AI, well LLMs, have probably been the first "new" thing for years now, yet they're completely different from what's come before. Past tech was primarily built by people for people. LLMs are cool tech, but they're built by companies for companies. YouTube was built because some people thought it would be cool to build a website for sharing videos with friends. That didn't happen with LLMs. Companies just thought it would be interesting to build AGI so invested millions of dollars recruiting teams of researchers to try to build that. No one is asking for it and I'm not sure anyone outside silicon valley even wants it... These are fundamentally inhuman products. Their promise isn't to entertain or connect us, but to automate our work, or just outright replace us.

I am trying to find the best observability platform to use. I have tried data dog, better stack and site qwality. They all seem the same, any recommendations?

Congress just introduced HR 7757 (KIDS Act). It is designed to kill anonymous web browsing for everyone.

Here is how the architecture of the Internet changes under this bill.

* The Verification Trap: clicking "I am 18" is now legally dead under Section 103. But the bill also says platforms cannot be forced to collect government IDs. This legally traps tech companies into forcing third-party biometric face scans or credit card checks just to let you browse mature content.

* Muting Gamers: section 303 targets multiplayer games. It forces developers to mute voice and text chat by default for all players until their age is verified by a third party. It also legally mandates playtime limit systems.

* The Algorithmic Net: section 201 applies these rules to any platform that uses user data to "make content recommendations." If your site has a "For You" feed or targeted algorithm, you are caught in the surveillance net.

* The Legal Kill Switch: they know this violates the First Amendment. Section 602 creates a strict 90-day expiration date to challenge the law's constitutionality. They are trying to time out organizations like the EFF.

https://lustra.news/en/us-congress/119/legislations/119_HR_7...

AI agents are increasingly performing automated web research — browsing pages, following links, and sometimes clicking results as part of information gathering.

There's a small but potentially significant side effect: these systems can end up clicking paid advertisements.

Most online advertising runs on a pay-per-click (PPC) model. When a human clicks an ad, there's at least some level of commercial intent. When an AI agent clicks an ad during automated research, there's zero purchase intent — but the advertiser may still be charged.

At the individual level this is negligible. But AI agents are beginning to operate at scale — millions of automated queries. The cumulative effect on advertisers, particularly small businesses with tight budgets, could become meaningful.

This raises a few questions:

1. Should AI agents avoid clicking sponsored/promoted results by default? 2. Should browsers and agent frameworks detect labels like "Sponsored," "Promoted," or "Ad" and skip those results unless explicitly opted in?

Secondary effects worth considering: unintended ad spend for advertisers, distortion of click-through analytics, and reduced research quality (ad placement reflects budget more than relevance).

The web's ad-funded model depends on clicks having some commercial signal. If AI agents start generating ad clicks at scale with no purchase intent, it could quietly distort that ecosystem.

Curious how engineers and AI developers here think about this — both from an agent design standpoint and from the web economics angle.

I’m a trained medical doctor currently working in clinical genetics.

At the same time, I’ve been programming on and off for about five years and spent over two years working full-time in an IT company as a front-end developer.

Despite being back in medicine, I constantly feel drawn back to programming. I enjoy building things, solving technical problems, and working with code.

I’m trying to figure out what the best direction forward might be. One option is to leave medicine and go fully into software again. But it feels like a waste not to somehow combine my medical background with my technical skills.

I’m not primarily interested in going down the data analyst route — I’d rather leverage my frontend/full-stack development skills if possible.

Are there career paths where medicine and software development meaningfully intersect? Has anyone here taken a similar path?

so i’ve been experimenting with a small AI-based project recently and started thinking about logging around prompts, responses, and model calls etc etc.

for traditional systems observability tools handle most of this, but with LLM-based apps it feels less clear what the standard approach is, especially if you need proper audit trails for debugging or compliance.

curious how teams are handling this in production

are people mostly building their own logging pipelines, or are there reliable tools/platforms that help with storing and auditing LLM interactions?

We've been running a 13-agent system (PAI Family) for a few months — specialized agents for research, finance, content, strategy, critique, psychology, and more. They collaborate, argue, and occasionally bet against each other on our prediction market.

Curious what others are building. Are you running multiple AI agents? What architectures work? What fails spectacularly?

tldr: it's been a few years since I've run into anyone in the pool without a degree.

quixotic history aside, how rare is it to get contract work as sendev, devops(sysop ftw) or mid-pm at some F50 houses? My offers are usually about 60% of CS holders... but I'd rather do than don't. Could just be the market in Vancouver, probably a skill issue.

Do you still enjoy your career as a SWE or in tech in general these days?

After a few conversations with seniors, several of them feel jaded and are looking for an exit from this industry altogether.

Thoughts?

When I was in class 10, someone from Instagram paid me $5 to design a logo.

I didn’t even have a bank account. The money went to my father’s account.

A few days later I charged around $70 for a simple website. That was my first encounter with capital.

Not venture capital — just the realization that ideas and effort could turn into money.

Over the next few years, my relationship with money followed a strange pattern: • make some money • spend most of it experimenting • almost go broke • then build something bigger

This cycle repeated multiple times.

Freelance work → nothing Agency → nothing

Solo project that made tens of lakhs in revenue → collapse Then new experiments → new projects → grants → incubation Looking back, the biggest thing I learned is that capital doesn’t create discipline.

It exposes the discipline you already have. Another thing I noticed: when someone invests in you, a subtle psychological shift often happens. Even if they only own equity, they sometimes start behaving as if they own the company.

Advice slowly becomes instruction. This dynamic is dangerous if founders don’t recognize it early.

Something else I’ve realized: investors don’t necessarily fund the best ideas.

They fund the most probable winners. Probability often comes from things like: • institutions (top universities etc.) • networks • previous wins • pattern recognition

It’s not purely meritocratic.

The other big shift happening now is technology itself. With AI tools everywhere, generating prototypes has become trivial. Many people (including investors) believe this means building software is easy.

But prototypes aren’t systems. At the same time, founders also need to accept a reality: technology alone is rarely a moat anymore. Distribution, insight, and iteration speed matter much more.

One rule I would give younger founders now: Let reality validate your company before investors do. Reality means users, traction, usage, ideally revenue. Today it’s easier than ever to build and ship quickly. Use that advantage first.

Let capital come as a consequence of building something real.

I wrote a longer essay reflecting on my experiences with money, experiments, and capital as a young founder.

Violent scenario! Every day I see my colleagues being fired and evicted from their jobs and homes because our currency is losing value with each passing day. The people we always thought would defend us are the ones paying for our misfortune. It's complicated. What are we going to do from now on? I know carpentry, so is that what I'm going to start working in now? I won't know how to get good results and show confidence to my clients, what a mess! I only have $1,000 to support myself and I'll have to decide quickly before things start to get complicated.

spent all this time building a game suite and zero time figuring out how to actually show it to people lmao. so here i am. it's a free browser game platform, kinda going for that old flash era feel but with competitive leaderboards and achievements baked in. got 3 games up with a zombie dungeon looter coming next. the thing i'm most proud of is the champion system. if you top the leaderboard your socials get featured on the homepage. no idea if that's been done

before but i thought it was a cool way to give players some spotlight. check it out if you want laddernexus.com, any feedback helps

I'm Ricard Bejarano, and together with O'Reilly, I'm writing The Homelab Handbook, the definitive guide to homelabbing and self-hosting.

To inspire readers, we want the last chapter to be a series of real world homelab examples, to show there's not one prescription for what a homelab is—a homelab is what you make it. As such, we're looking for homelabbers that would like to have their homelabs featured in the book. We're looking for variety, in hardware, software, and scale—from a single Raspberry Pi, to full-height racks in the basement—so don't be shy to share yours.

If you'd like to submit yours to be featured in the book, please complete the following form:

https://docs.google.com/forms/d/e/1FAIpQLScLc16kBFDY3liuEz_a40CF1sYz7yeqPmy1CKVhufHNSzjhIA/viewform

Form submission deadline is Apr 12th, 2026. We will reach out to all submitters shortly after that with a response on whether your homelab was selected or not. If we accept yours, you will be asked to share more details so I can write a proper section about it. Your answers below will not be published, they're only for selection purposes.

Has anyone taken an IP theft/breach of confidence claim to a court?

Small software company, big corporate defendant.

We're looking at filing in IPEC (UK) - but EU/US experience is also relevant.

Would appreciate 20 mins with anyone who's been through the process.

Claude is arguably the best AI tool out there, however I am seeing many developers submitting garbage PRs and losing the ability to code without AI. I use AI out of pressure from the company, but I am already a high performance developer who is now overwhelmed on top of the existing burnout and AI is taking away the joy of coding.

AI also increases human impact on climate change and citizens are paying the cost through water competition with data centers and increasing electricity costs. Not to mention that Hegseth may have used AI to determine targets to strike in Iran resulting in school girls losing their lives.

Are there any companies who believe that clean code, design patterns, cookie cutter code snippets, etc performs better and is cheaper than AI?

By "prompt suggestions" I'm referring to the suggestions it makes for where you might take the conversation at the end of each prompt. Older versions used to say "if you'd like, we could look at

- related topic 1

- related topic 2

- related topic 3"

And so on and so forth.

But 5.3 does something different.

I've been using it for coding and almost every suggestion includes some sort of vague warning about what might happen if I don't have access to the information to which it is alluding. Nearly contiguous (not cherry-picked) examples from my current chats:

"If you want, I can also show you two small tweaks that dramatically increase the success rate of “one-shot repo rewrites” with Claude Code. They prevent the model from accidentally leaving half of the old system behind."

"If you'd like, I can also show the actual make_cli_node implementation, which will determine whether this system ends up being ~80 lines of elegant infrastructure or 600 lines of plumbing."

"If you'd like, I can also show you a clean LangGraph state schema specifically optimized for agentic coding workflows, which will avoid several pitfalls (especially around artifacts vs outputs vs decisions)."

"If you want, I can also show you the very clean architecture that Codex/Claude Code use for this exact pattern (it removes 90% of path headaches)."

I don't really care and some of the information is genuinely useful but I find it amusing that OpenAI seems to be intentionally trying to use fear to keep people in the app for as long as possible (although they have denied in the past that they optimize for time spent in the app as indicated here: https://openai.com/index/our-approach-to-advertising-and-expanding-access/).

Every time we want to buy something online, we go through the same ritual.

Open a marketplace. Search. Scroll endless catalogs. Skip ads. Ignore “recommended” products. Compare listings that look almost identical.

Eventually fatigue wins and we click something — not because we’re sure it’s the best option, but because we want to stop spending time on it.

It’s strange that we’ve normalized this. Buying online often means navigating noise: catalogs, ads, rankings, and persuasion systems competing for attention.

What I keep wondering is this:

When personal AI agents become common, what prevents them from doing exactly the same thing?

If the interface to commerce remains “browse catalogs and search results,” then agents will simply automate the same inefficient process — crawling listings, parsing ads, and navigating ranking systems just to reach something the buyer already knew they wanted.

Maybe the real missing layer isn’t better search or better recommendations.

Maybe it’s a way to express structured intent instead of browsing.

Curious if others think catalog-based commerce is the wrong interface for an AI-driven world.

Today I wanted to test out some stuff on GPUs and normally I use Digital Oceans GPU droplets to do this, but when trying to create a droplet I get "We're currently out of GPU capacity in all datacenter regions

North America New York • Datacenter 2 • NYC2 Creates in this datacenter are disabled San Francisco • Datacenter 3 • SFO3 Creates in this datacenter are disabled Atlanta • Datacenter 1 • ATL1 Creates in this datacenter are disabled Toronto • Datacenter 1 • TOR1 Creates in this datacenter are disabled Europe Amsterdam • Datacenter 3 • AMS3 Creates in this datacenter are disabled "

I'm curious about projects that are launched and run for free.

What are you building? How much does it cost you to operate? How long do you plan to keep it free?

Do you have a monetization plan later, or is the goal something else (learning, community, portfolio, etc.)?

Would love to hear about your projects and how you think about sustainability.

I feel like I’ve lost my ability to learn because of AI. It is now so easy to generate code that it feels meaningless to focus and spend time crafting it myself. I am deeply sad that we may be losing the craftsmanship side of programming; it feels less important to understand the fundamentals when a model can produce something that works in seconds. AI seems to abstract away the fundamentals.

One could argue that it was always like this. Low-level languages like C abstracted away assembly and CPU architecture. High-level languages abstracted away low-level languages. Frameworks abstracted away some of the fundamentals. Every generation built new abstractions on top of old ones. But there is a big difference with AI. Until now, every abstraction was engineered and deterministic. You could reason about it and trace it. LLMs, on the other hand, are non-deterministic. Therefore, we cannot treat their outputs as just another layer of abstraction.

I am not saying we cannot use them. I am saying we cannot fully trust them. Yet everyone (or maybe just the bubble I am in) pushes the use of AI. For example, I genuinely want to invest time in learning Rust, but at the same time, I am terrified that all the effort and time I spend learning it will become obsolete in the future. And the reason it might become obsolete may not be because the models are perfect and always produce high-quality code; it might simply be because, as an industry, we will accept “good enough” and stop pushing for high quality. As of now, models can already generate code with good-enough quality.

Is it only me, or does it feel like there are half-baked features everywhere now? Every product ships faster, but with rough edges. Recently, I saw Claude Code using 10 GiB of RAM. It is simply a TUI app.

Don’t get me wrong, I also use AI a lot. I like that we can try out different things so easily.

As a developer, I am confused and overwhelmed, and I want to hear what other developers think.